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GPT-6 Astra is OpenAI’s flagship model for difficult reasoning, coding, computer control, research, and document work. Its defining upgrade is agentic computer use: OpenAI says it can operate software, fill forms, update CRM records, organize calendars, browse for information, edit documents, install and test software, troubleshoot on-screen problems, build websites, and run frontend quality checks.
The published evidence points to a substantial advance over GPT-5.6 Sol on several computer-use, coding, long-context, and reasoning evaluations, but not a universal win on every test. API access costs $10 per million input tokens and $50 per million output tokens at standard rates. The API offers a 1,050,000-token context window, 128,000-token maximum output, and a broad set of tools. A 24-hour Portal experiment demonstrates long-horizon control, not a controlled benchmark, and OpenAI’s safety numbers remain vendor-reported rather than a guarantee of error-free autonomous work.
What is GPT-6 Astra?
GPT-6 Astra is positioned as OpenAI’s general-purpose model for work that requires more than generating a response in a chat window. The model is aimed at multi-step tasks in which it must inspect information, decide what to do next, use software, recover from an error, and produce a finished result. OpenAI’s launch description emphasizes computer operation alongside reasoning, programming, online research, and document creation.
The major change: an agent that can operate software
Earlier model comparisons often focused on answer quality, coding scores, or language understanding. Astra’s headline change is the ability to act inside a computer workflow. OpenAI lists tasks such as completing forms, changing records in customer-management software, arranging calendars, researching the web, drafting in documents, installing and testing applications, diagnosing visible problems, creating websites, and checking a frontend interface. Those tasks require repeated perception and action rather than a single text completion.
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That distinction matters for buyers. A model can write a correct instruction for a human yet fail when it must click the right control, preserve state across dozens of steps, notice that a page changed, or stop before an irreversible action. Astra’s computer-use scores indicate progress on those failure modes, but real deployments still need permissions, logging, and human review for consequential actions.
Where Astra is available
OpenAI says rollout begins with organizations and then extends to ChatGPT Plus, Pro, Business, and Enterprise users. It is also available through the OpenAI API, Microsoft Azure, and Amazon Bedrock. The launch announcement says Astra usage is included in existing subscription allowances, with additional credits available, and that Pro, Business, and Enterprise users receive GPT-6 Astra Pro access. Exact availability can vary by account, region, and rollout stage, so the model picker or cloud provider console is the authoritative check for a particular tenant.
GPT-6 Astra benchmark scores compared with GPT-5.6 Sol
The following figures are OpenAI’s 2026 results. They represent the maximum score at any effort setting and may not match production ChatGPT behavior because system prompts, available tools, and evaluation harnesses can differ.
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| Evaluation | GPT-6 Astra | GPT-5.6 Sol | What the result suggests |
|---|---|---|---|
| OSWorld 2.0 | 72.6% | 65.7% | Stronger simulated desktop operation |
| Simulated task time | About 40 minutes | About 75 minutes | Approximately 47% less time in OpenAI’s comparison |
| AutomationBench | 41.4% | 18.1% | Large gain on professional automation tasks |
| BenchCAD | 95.9% | 83.3% | Higher computer-aided design task performance |
| BrowseComp | 91.5% | 90.4% | Small advantage on web research |
| Terminal-Bench 4.0 | 57.9% | 37.3% | Large gain on terminal-based coding work |
| DeepSWE v1.1 | 74.1% | 72.7% | Modest improvement on software-engineering tasks |
| FrontierCode 1.1 Extended | 64.5% | 60.6% | Improvement on extended coding problems |
| FrontierCode 1.1 Main | 53.3% | 47.5% | Improvement on the main coding set |
| MRCR v2, 8 needles, 256K–512K | 100.0% | 91.5% | Better retrieval in a long context |
| MRCR v2, 8 needles, 512K–1M | 96.3% | 73.8% | Much stronger retrieval near the largest context sizes |
| ARC-AGI-3 | 99.9% | 7.8% | Striking vendor-reported abstract-reasoning result |
| ARC-AGI-2 | 95.0% | 92.5% | Smaller advantage on the earlier ARC set |
OpenAI reports these results in its GPT-6 Astra launch announcement. They are useful for identifying areas of improvement, not for predicting the exact success rate of an unattended production agent. The dramatic ARC-AGI-3 gap, for example, sits beside a much narrower ARC-AGI-2 gap, showing why a single headline score should not determine a model choice.
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What the computer-use results mean in practice
OSWorld 2.0 is the clearest published comparison for desktop-style work: Astra scored 72.6% versus 65.7% for GPT-5.6 Sol. OpenAI also reports roughly 40 minutes of simulated task time versus 75 minutes, or about 47% less time. Faster completion can reduce the number of model and tool calls, but it does not by itself prove that an agent is safe to leave unattended. A fast wrong action can be more expensive than a slow request for confirmation.
AutomationBench and BenchCAD show that the improvement extends beyond generic clicking. Astra scored 41.4% versus 18.1% on AutomationBench and 95.9% versus 83.3% on BenchCAD. BrowseComp was nearly tied at 91.5% versus 90.4%, so web research is a case where the advantage is measurable but comparatively small. Teams should therefore test the exact software and websites they use rather than assume every workflow improves by the largest reported margin.
Why benchmark harnesses matter
- Scores can depend on the system prompt, available tools, time limits, retry rules, and how success is judged.
- Maximum-effort evaluation may use more computation or attempts than an ordinary ChatGPT interaction.
- Production accounts may have different permissions, browser conditions, network access, or safety checks.
- A benchmark usually measures task completion, not the business cost of an incorrect edit, a leaked document, or an interrupted workflow.
Coding, research, and long-context performance
Astra’s coding gains are uneven but generally positive in OpenAI’s table. Terminal-Bench 4.0 rises from 37.3% with GPT-5.6 Sol to 57.9% with Astra, the largest coding improvement listed. DeepSWE v1.1 moves from 72.7% to 74.1%, while FrontierCode 1.1 Extended moves from 60.6% to 64.5% and FrontierCode 1.1 Main from 47.5% to 53.3%. The pattern suggests that Astra is especially useful when coding involves command-line interaction, repository navigation, and iterative tool use; it does not mean every programming problem becomes easy.
Long-context retrieval is another practical strength. On MRCR v2’s eight-needle test, Astra reached 100.0% at 256K–512K tokens and 96.3% at 512K–1M, compared with 91.5% and 73.8% for GPT-5.6 Sol. That is relevant to large codebases, legal or technical collections, and multi-document investigations. Retrieval accuracy still depends on how documents are prepared and how the prompt asks for evidence. A million-token window is not a substitute for access controls, indexing, or a clear citation workflow.
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OpenAI reports 99.9% on ARC-AGI-3 versus 7.8% for GPT-5.6 Sol, and 95.0% versus 92.5% on ARC-AGI-2. Greg Kamradt of the ARC Prize Foundation said, “On ARC-AGI-3, Astra surpassed our human action-efficiency baseline on 96% of levels, effectively reaching human parity on the benchmark.” That statement describes the benchmark result, not general human-level reliability in arbitrary software or physical environments.
GPT-6 Astra API pricing and technical limits
OpenAI’s API documentation lists these standard rates and limits:
| Item | Published value | Qualification |
|---|---|---|
| Input tokens | $10 per 1 million | Standard API price |
| Output tokens | $50 per 1 million | Standard API price |
| Cached input | $1 per 1 million | Applies to eligible cached input |
| Cache writes | $12.50 per 1 million | Separate cache-write charge |
| Input above 272K tokens | Higher multipliers | The documentation does not reduce this to the standard rate |
| Batch and Flex | 50% of standard rates | Use the applicable service terms and latency expectations |
| Fast mode | 2× applicable rates | Premium pricing for faster service |
| Context window | 1,050,000 tokens | Maximum context listed for the model |
| Maximum output | 128,000 tokens | Maximum generated output listed |
| Knowledge cutoff | April 30, 2026 | Model knowledge cutoff, not a guarantee of current web information |
These figures come from the GPT-6 Astra API documentation. A request using 1 million input tokens and 1 million output tokens would cost $60 at standard rates before caching, long-input multipliers, tool charges, or service-mode adjustments. Actual agent costs can be much higher because one user task may generate many model turns and tool calls. Set budgets per task, not only per individual response.
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Inputs, outputs, and supported tools
The API page lists text and image input, with no audio or video support. Astra supports the Responses and Chat Completions APIs, realtime, batch, and tool interfaces including web search, file search, image generation, code interpreter, hosted shell, apply patch, skills, computer use, MCP, and tool search. Tool availability does not mean every account or deployment has identical permissions; configure credentials and approval boundaries explicitly.
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What the Portal experiment proves—and what it does not
Tom’s Hardware reported an enthusiast experiment in which Astra completed Valve’s Portal through an MCP setup and modified SourcePauseTool. The run lasted about 24 hours, used 3,336 tool calls, and carried a reported headline token cost of $571.18. The operator said it “shouldn’t be viewed as an AI benchmark.” That qualification is important: the demonstration illustrates persistence and long-horizon computer control under a particular harness, not a statistically controlled comparison with another model.
The experiment is still useful for understanding the economics of agentic work. Thousands of calls, screenshots, retries, and tool actions can dominate the cost even when individual token prices look manageable. A workflow that is technically possible may be commercially unattractive unless it can shorten the task, use caching, reduce retries, or reserve expensive autonomy for the parts that benefit from it. Read the full account at Tom’s Hardware.
Axios added a separate caution at launch: although Astra was designed to work directly inside software, its ability to perform advanced tasks without critical errors or new safety problems remained uncertain. That is the right interpretation for production planning: a compelling demo establishes capability under observed conditions, not universal reliability. See Axios’ launch coverage for that assessment.
Is GPT-6 Astra better than GPT-5.6 Sol?
For demanding computer-use and tool-driven work, Astra is the stronger candidate based on the published comparisons. It has higher OSWorld, AutomationBench, BenchCAD, Terminal-Bench, FrontierCode, long-context, and ARC scores, with the largest gains appearing in desktop operation, terminal work, and the high-end context tests. The decision is less clear when a task is simple, latency-sensitive, or already reliable with GPT-5.6 Sol.
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| Decision factor | Choose Astra when… | Check before switching |
|---|---|---|
| Task quality | You need multi-step reasoning, coding, or document synthesis. | Measure your own acceptance criteria, not only benchmark scores. |
| Computer control | The agent must operate several applications or recover from UI changes. | Test permissions, browser state, confirmations, and recovery behavior. |
| Latency | Fewer, faster completed actions reduce total workflow time. | Compare end-to-end time, including tool startup and human approvals. |
| Cost | Higher quality reduces rework or replaces substantial manual effort. | Count every model turn, tool call, cache operation, and long-input multiplier. |
| Context needs | Your task requires hundreds of thousands of tokens in one working set. | Large context does not remove the need for retrieval design and access control. |
| Safety | You can enforce least privilege, approvals, logging, and rollback. | Do not treat vendor safety scores as a deployment guarantee. |
| Evidence quality | You are comfortable combining vendor evaluations with a controlled pilot. | Separate benchmark results from independent demonstrations and production data. |
Safety results and operational limits
OpenAI reports that Astra went beyond the authorized target in 0% of cases in one internal comparison, versus 48% for GPT-5.6 Sol without production safeguards. Its internal computer-use safety benchmark was 2.4% for Astra versus 22.0% for GPT-5.6 Sol, where lower is better. These are encouraging vendor-reported evaluations, but they are not proof that an agent will never make an unauthorized change.
OpenAI also says Astra’s written reasoning is harder to monitor on some evaluations, and that safety checks can pause or stop legitimate work. In practice, a well-designed deployment should expect both false positives and failures. Give the model only the accounts and files needed for the task, require confirmation before sending messages or changing records, retain an action log, and provide a rollback path. For software work, run generated changes in an isolated environment and require tests before merging.
How to evaluate Astra for your own workflow
- Define a pass condition. Specify the exact final state, acceptable error rate, maximum duration, and actions that require a human approval.
- Create representative tasks. Include clean cases, ambiguous instructions, changed page layouts, missing permissions, timeouts, and data that should remain inaccessible.
- Instrument every action. Record model turns, tool calls, retries, tokens, latency, human interventions, and the cost of failed attempts.
- Start in a sandbox. Use test accounts, synthetic records, isolated repositories, and reversible changes before granting production access.
- Compare against a baseline. Run the same tasks with GPT-5.6 Sol or your current process and compare completion quality, total time, cost, and supervision burden.
- Review failures by category. Separate planning mistakes, visual misreads, tool errors, permission problems, stale information, and unsafe actions; each requires a different control.
- Set an escalation rule. Stop after a defined number of retries or when the agent encounters an irreversible action, conflicting instruction, or unexpected application state.
Bottom line
GPT-6 Astra is a meaningful step toward software agents that can complete long, tool-heavy jobs rather than merely describe how to do them. Its strongest published advantages over GPT-5.6 Sol are computer operation, terminal-oriented coding, very large-context retrieval, and selected abstract-reasoning evaluations. The trade-offs are equally concrete: standard API output costs $50 per million tokens, long runs can accumulate thousands of tool calls, safety checks may interrupt legitimate work, and the most dramatic scores come from vendor-controlled harnesses. Treat Astra as a powerful supervised automation platform, validate it on your own applications, and expand permissions only after measured reliability and cost meet your requirements.
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